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Call for Papers
Workshop on Data Mining for Fake News in Social Media:
Propagation,
Detection, and Mitigation (FEND'19), in conjunction with SDM'19
http://pike.psu.edu/fend19/
May 2-4, 2019, Alberta, Canada
Social media has become a popular means to consume news. However,
the
quality of news on social media is lower than traditional news
organizations. Because it is cheap to provide news online and much
faster
and easier to disseminate through social media, large volumes of
fake news,
i.e., those news articles with intentionally false information,
are
produced online for a variety of purposes, such as financial and
political
gain. The extensive spread of fake news can have severe negative
impacts on
individuals and society. First, fake news can break the
authenticity
balance of the news ecosystem. For example, it is evident that the
most
popular fake news was even more widely spread on Facebook than the
most
popular authentic mainstream news during the U.S. 2016
presidential
election. Second, fake news intentionally persuades consumers to
accept
biased or false beliefs for political or financial gain. For
example, in
2013, $130 billion in stock value was wiped out in a matter of
minutes
following an Associated Press (AP) tweet about an explosion that
injured
Barack Obama. AP said its Twitter account was hacked. Third, fake
news
changes the way people interpret and respond to real news,
impeding their
abilities to differentiate what is true from what is not.
Therefore, it's
critical to understand how fake news propagate, developing data
mining
techniques for efficient and accurate fake news detection and
intervene in
the propagation of fake news to mitigate the negative effects.
The objectives of this workshop are:
- Bring together researchers from both academia and industry as
well as
practitioners to present their latest problems and ideas;
- Attract social media providers who have access to interesting
sources of
fake news datasets and problems but lack the expertise in data
mining to
use data effectively;
- Enhance interactions between data mining, text mining, social
media
mining, and sociology and psychology communities working on
problems of
fake news propagation, detection, and mitigation.
This workshop aims to bring together researchers, practitioners
and social
media providers for understanding fake news propagation, improving
fake
news detection in social media and mitigation.
Topic areas for the workshop include (but are not limited to) the
following:
- User behavior analysis and characterization for fake news
detection
- Text mining - mining news contents and user comments
- Early fake news detection
- Unsupervised fake news detection
- Fact-checking
- Tracing and characterizing the propagation of fake news and true
news
- Malicious account and bot detection, user credibility assessment
- Visual analysis and exploration with images on the news
- News event aggregation and detection
- Building benchmark datasets for fake news detection in social
media
Paper Submission:
Papers should be submitted as PDF, using the SIAM conference
proceedings
style, available at
https://www.siam.org/Portals/0/Publications/Proceedings/soda2e_061418.zip?ver=2018-06-15-102100-887.
Submissions should be limited to nine pages and submitted via CMT
at
https://cmt3.research.microsoft.com/FEND2019.
Important Dates:
Submission deadline: February 1, 2019
Notification: March 15, 2019
SDM pre-registration deadline: April 2, 2019
Camera ready: April 15, 2019
Conference dates: May 2-4, 2019
Shall you have any questions, please email to
szw494@psu.edu or
kai.shu@asu.edu.
Workshop Organizers:
Suhang Wang Penn State University, USA
Dongwon Lee Penn State University, USA
Huan Liu Arizona State University, USA
Workshop Publicity Chair:
Kai Shu Arizona State University, USA
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